Machine learning algorithms have recently made rapid progress using deep neural networks (DNNs). DNNs are artificial neural networks that have multiple hidden layers between input and output layers. Example types of DNNs include recurrent neural networks (RNNs) and convolutional neural networks (CNNs). DNNs have broad application in the fields of artificial intelligence, computer vision, automatic speech recognition, language translation, and so on. Training times, memory requirements, and energy efficiency remain challenges associated with DNNs. Moreover, different DNN architectures are more efficient for different tasks. For example, CNNs may be more efficient than other types of DNNs for image recognition while RNNs may be more efficient than CNNs for natural language translation.
Searching recorded audio for instances of a keyword is a time-consuming activity for humans. For instance, it may take hours for a person to listen to recorded audio to find a part of the recorded audio that the person was looking for. To date, machine learning algorithms to perform this activity have met with significant challenges.